This index groups my posts by themes so you can find what you are looking for more easily. The themes are listed here at the top - choose your theme to find the posts that fall under that category.

Themes

• Reading Papers

• CS Coding Interview

• MolGAN Work

• The Silver Challenge

• The Grad Student Meta-Game

• Machine Learning and Artificial Intelligence Topics

  • Variational Autoencoders

  • Filtering

  • Review of Entity Abstraction

  • Probabilistic Graphical Models

  • Time Series Forecasting

• Computing Tools

• DNA Nanotechnology

• Control Theory

  • PhD Qualifying Exam

  • Advanced Topics in Controls

  • Robot Kinematics and Dynamics

• Social Issues

• Miscellaneous

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Index

Reading Papers

Short summaries on papers I read for fun and learning.

• Week of May 17 Paper Reading

• Week of May 24 Paper Reading

• Week of May 31 Paper Reading

• Week of July 5 Paper Reading

CS Coding Interview

Posts on topics that are commonly tested in a coding interview.

• A Framework for Solving Dynamic Programming Problems

• Kadane’s Algorithm

• Bit Manipulation

• Dynamic Programming

• Recursion(recursion)

• Backtracking Algorithm

• Basic Search and Sort Algorithms

• Depth-First Search and Breadth-First Search

• Heaps

• Obligatory Stacks and Queues Post

• A Quick Note on Graph Implementations

• Tries and Suffix Trees

• Binary Search Trees

• Mutable and Immutable Objects in Python

• Dynamically Resized Lists and Arrays

• Hash Tables

• Linked Lists

MolGAN Work

Posts related to the 2018 paper by De Cao and Kipf on MolGAN, a graph GAN that generates novel molecules.

• Applying GANs to Graphs

• Graph Convolution

• Putting the “Relational” in R-GCNs

• Graph Theory for Relational Graph Convolutional Networks (R-GCNs)

• Bringing R-GCNs Back to MolGAN

• Wasserstein GANs

• Introduction to Policy Methods in RL

• Building Up to DDPG

• DDPG and MolGAN, Finally

The Silver Challenge

Daily reflections on the RL course taught by David Silver.

• The Silver Challenge - Lecture 1

• The Silver Challenge - Lecture 2

• The Silver Challenge - Lecture 3

• The Silver Challenge - Lecture 4

• The Silver Challenge - Lecture 5

• The Silver Challenge - Lecture 6

• The Silver Challenge - Lecture 7

• The Silver Challenge - Lecture 8

• The Silver Challenge - Lecture 9

• The Silver Challenge - Lecture 10

The Grad Student Meta-Game

Skills I am learning while in grad school.

• Staying on Top of the Literature Needs a rewrite!

• How to Conference

• A Reflection on Writing My First Grant Proposal

• Reflections and New Goals in the PhD Program in 2021

• Reflections and New Goals for 2022

Machine Learning and Artificial Intelligence Topics

Posts that I wrote to teach myself about ideas in ML and AI.

• A Brief Introduction to Reinforcement Learning

• A Brief Introduction to Machine Learning

• Linear Regression and Gradient Descent

• Brain, Meet Neural Networks

• Logistic Regression

• How Neural Networks Learn

• Bias and Variance in Neural Networks

• Overfitting and Regularization

• Support Vector Machines

• MLE vs MAP

• The Reparameterization Trick

• Binary Cross-Entropy

• The Gumbel-Softmax Distribution

• Review of “Scene Graph Generation by Iterative Message Passing” by Xu et al.

• Bayesian Optimization for Beginners

• A Brief Survey of Machine Learning Regression Models

Variational Autoencoders

• ML Bonanza Episode 1 - Basics of VAEs

• ML Bonanza Episode 2 - The Loss Function

• ML Bonanza Episode 3 - Gradient of the ELBO

Filtering

• Tracking and Filtering

• Kalman Filters

• Particle Filters

Review of Entity Abstraction

• Review of “Entity Abstraction in Visual Model-Based Reinforcement Learning”

• Review of “Entity Abstraction”, Part 2

• Review of “Entity Abstraction”, Part 3

Probabilistic Graphical Models

• Introduction to Graph Networks

• Probability and Graph Networks

• Graph Networks - Putting It All Together

• Welcome to Factor Graphs

• Understanding Message Passing in GNNs

• Inference with Probabilistic Graphical Models

• Sampling Methods for Approximate Inference

• Message Passing Algorithms

Time Series Forecasting

• WiDS 2023 Datathon

• Using the ARIMA Model for Forecasting

• Using ML Models for Time Series Forecasting

• Performing an EDA, with a Focus on Correlation, for Time Series Forecasting

• A Brief Introduction to Time Series Forecasting

Computing Tools

• Lessons in Writing Code, Part 1

• How to Git

• Git with the Flow

• Docker Basics

• Docker Best Practices

• Google’s Python Style Guide Part 1 - Functionality

• Writing Dynamics Models in MATLAB

• ODE Solvers in MATLAB

• A High-Level Overview of Cloud Computing

• A First Foray into Using QGIS

DNA Nanotechnology

For someone who works with DNA, I know an embarassingly small amount about this amazing molecule. Read these to learn the basics and maybe even some cool stuff about DNA nanotechnology.

• Building Things with DNA

• The Structure of DNA

• Nitrogenous Bases and DNA

• Explaining the Scallop Theorem

• Modeling DNA

• The Diffraction Limit

• What is FRET?

• Designing DNA Origami Structures

• Molecular Dynamics Simulation

• Remembering Dynamic Light Scattering from 2 Years Ago

• Shining Some Light on Spectrophotometry

• Introducing Atomic Force Microscopy

• A Brief Guide to AFM Probe Selection

• AFM Scanning Parameters and Image Artifacts

• Thermodynamics and Reaction Kinetics, Revisited

• Cooperativity is a Key to Life!

• Deriving the Boltzmann Distribution

• The Law of Mass Action

• More Cooperativity Math!

• A Review of the Fundamentals of Statistics

• Common Statistical Hypothesis Tests

Control Theory

PhD Qualifying Exam

• Lead Compensators

• Drawing Root Locus Plots

• Analyzing Root Locus Plots

• Steady State Error

• The Fourier and Laplace Transforms

• Bode Plots

• LTI Systems

• Lead Lag Compensators

• Stability in Classical Control

• Linearization

• Gain and Phase Margins

• Drawing Nyquist Plots

• Classical Control Theory Toolbox

• Fundamentals of Lead and Lag Compensators

• How to Derive Equations of Motion

• Example 9.1 (Nise)

• Example 9.2 (Nise)

• Example 9.3 (Nise)

• Skills Assessment 9.1 (Nise)

• Linearizing an Inverted Pendulum

• Non-Minimum Phase Systems

• The Taylor Series

• Compensator Design with Bode Plots

• Example 11.1 (Nise)

• More on Nyquist Fundamentals

• Example 11.3 (Nise)

• Example 11.4 (Nise)

• Designing Lead and Lag-Lead Compensators with Bode Plots

Advanced Topics in Controls

• Control Theory Grab Bag and MPC

• Welcome to Discrete Time

• Writing System Dynamics in Discrete Time

• Convex Optimization and MPC

Robot Kinematics and Dynamics

• Fundamentals from Murray, Li and Sastry

• Spatial Velocity vs. Body Velocity

• Getting Some Intuition for Matrix Exponentials

• Motion Planning with Calculus

Social Issues

Posts written while contemplating various social issues.

• Thoughts on Education

• COVID-19

• Book Reviews from 2019

Miscellaneous

I couldn’t fit these posts into any other category.

• Hello World!